Features are individual measurable inputs used to train algorithms. Essentially, they are the attributes or properties that are fed into models to help them make predictions or classifications. In a dataset about housing prices, for example, features could include the number of bedrooms, square footage, and location. Quality feature engineering, which involves selecting the most relevant features, is crucial for building effective predictive models.